e5dd5b5593
## 核心功能 ### 1. 成交量序列分析 (volume_price_sequence.py) - 按累计成交量排序的价格趋势分析 - 三合一综合图表：价格序列+成交量分布+时间序列 - 关键价格水平自动标注 ### 2. 成交量分布深度分析 (volume_distribution_analysis.py) - 7种专业可视化图表 - 统计特征分析和分布拟合 - 交易模式识别和业务洞察 ### 3. 大额订单分析工具集 (large_orders/) - 买1/卖1量大单分析 (阈值99) - 买卖挂单合计分析 (阈值200) - 当前成交量分析 (阈值150) - 信号抑制优化算法 (38%抑制率) ## 技术特性 - 信号抑制算法：有效减少重复信号干扰 - 多维度分析：支持多种信号类型 - 专业可视化：四宫格综合分析图 - 业务洞察：基于数据的交易建议 ## 分析结果 - 卖1量大单：短期下跌，长期大幅上涨反转 - 买挂合计：各时间窗口小幅正收益 - 信号抑制：短期收益从-0.0778提升至+0.1347 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
586 lines
25 KiB
Python
586 lines
25 KiB
Python
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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AU2512期货当前成交量分布分析工具
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这个脚本专门用于分析期货交易中当前成交量的分布特征，
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提供深入的成交量模式分析和可视化展示。
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使用方法:
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python volume_distribution_analysis.py [data_file]
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如果不指定文件，默认分析 data/au2512_20251013.parquet
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"""
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import pandas as pd
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import numpy as np
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import matplotlib.pyplot as plt
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from matplotlib.gridspec import GridSpec
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import seaborn as sns
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from datetime import datetime
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import warnings
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import os
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import sys
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import argparse
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from pathlib import Path
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from scipy import stats
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warnings.filterwarnings('ignore')
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class VolumeDistributionAnalyzer:
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"""当前成交量分布分析器"""
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def __init__(self, data_file=None):
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"""
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初始化分析器
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Args:
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data_file (str): 数据文件路径，默认为 data/au2512_20251013.parquet
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"""
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self.data_file = data_file or "data/au2512_20251013.parquet"
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self.df = None
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self.current_volume = None
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self.setup_chinese_font()
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def setup_chinese_font(self):
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"""设置中文字体支持"""
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try:
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# 直接使用已知可用的中文字体
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chinese_fonts = ['Microsoft YaHei', 'SimHei', 'SimSun', 'KaiTi', 'FangSong', 'DengXian']
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for font in chinese_fonts:
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try:
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plt.rcParams['font.sans-serif'] = [font]
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plt.rcParams['axes.unicode_minus'] = False
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plt.rcParams['font.size'] = 10
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plt.rcParams['axes.titlesize'] = 12
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plt.rcParams['axes.labelsize'] = 10
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plt.rcParams['xtick.labelsize'] = 9
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plt.rcParams['ytick.labelsize'] = 9
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# 简单测试字体
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fig, ax = plt.subplots(figsize=(1, 1))
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ax.text(0.5, 0.5, '测试', fontsize=12)
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plt.close(fig)
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print(f"[*] 成功设置中文字体: {font}")
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return
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except:
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continue
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# 如果都失败，使用默认设置
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print("[!] 无法设置中文字体，使用默认字体")
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plt.rcParams['font.sans-serif'] = ['DejaVu Sans']
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plt.rcParams['axes.unicode_minus'] = False
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plt.rcParams['font.size'] = 10
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except Exception as e:
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print(f"[!] 字体设置警告: {e}")
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plt.rcParams['font.sans-serif'] = ['DejaVu Sans']
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plt.rcParams['axes.unicode_minus'] = False
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plt.rcParams['font.size'] = 10
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def load_data(self):
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"""加载数据"""
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try:
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if not os.path.exists(self.data_file):
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raise FileNotFoundError(f"数据文件不存在: {self.data_file}")
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print(f"正在加载数据: {self.data_file}")
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# 根据文件扩展名选择读取方式
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if self.data_file.endswith('.parquet'):
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self.df = pd.read_parquet(self.data_file)
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elif self.data_file.endswith('.csv'):
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self.df = pd.read_csv(self.data_file)
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else:
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raise ValueError("不支持的文件格式，请使用 .parquet 或 .csv 文件")
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print(f"数据加载成功: {len(self.df):,} 条记录")
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return True
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except Exception as e:
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print(f"数据加载失败: {e}")
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return False
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def prepare_volume_data(self):
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"""准备当前成交量数据"""
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print("\n=== 准备成交量数据 ===")
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# 确保有当前成交量列
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if '当前成交量' not in self.df.columns:
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cumulative_col = None
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for col in self.df.columns:
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if '累计' in col and '成交量' in col:
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cumulative_col = col
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break
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if cumulative_col is None:
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raise ValueError("无法找到累计成交量列")
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print(f"使用累计成交量列: {cumulative_col}")
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self.df['当前成交量'] = self.df[cumulative_col].diff().fillna(0)
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self.df.loc[self.df.index[0], '当前成交量'] = self.df.loc[self.df.index[0], cumulative_col]
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# 提取当前成交量数据
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self.current_volume = self.df['当前成交量'].copy()
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# 移除零值和异常值（超过99.9%分位数的值）
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q99_9 = self.current_volume.quantile(0.999)
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self.current_volume_clean = self.current_volume[
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(self.current_volume > 0) & (self.current_volume <= q99_9)
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]
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print(f"原始成交量数据点: {len(self.current_volume):,}")
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print(f"有效成交量数据点: {len(self.current_volume_clean):,}")
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print(f"移除零值和异常值后: {len(self.current_volume_clean):,} 个")
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return True
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def calculate_basic_statistics(self):
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"""计算基础统计信息"""
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print("\n=== 基础统计分析 ===")
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self.stats = {
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'total_ticks': len(self.current_volume),
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'valid_ticks': len(self.current_volume_clean),
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'zero_volume_ticks': sum(self.current_volume == 0),
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'mean_volume': self.current_volume_clean.mean(),
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'median_volume': self.current_volume_clean.median(),
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'std_volume': self.current_volume_clean.std(),
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'min_volume': self.current_volume_clean.min(),
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'max_volume': self.current_volume_clean.max(),
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'q25': self.current_volume_clean.quantile(0.25),
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'q75': self.current_volume_clean.quantile(0.75),
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'q90': self.current_volume_clean.quantile(0.90),
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'q95': self.current_volume_clean.quantile(0.95),
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'q99': self.current_volume_clean.quantile(0.99)
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}
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print(f"总Tick数: {self.stats['total_ticks']:,}")
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print(f"有效成交量Tick数: {self.stats['valid_ticks']:,}")
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print(f"零成交量Tick数: {self.stats['zero_volume_ticks']:,}")
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print(f"平均成交量: {self.stats['mean_volume']:.2f} 手")
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print(f"中位数成交量: {self.stats['median_volume']:.2f} 手")
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print(f"成交量标准差: {self.stats['std_volume']:.2f} 手")
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print(f"最小成交量: {self.stats['min_volume']:.2f} 手")
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print(f"最大成交量: {self.stats['max_volume']:.2f} 手")
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print(f"25%分位数: {self.stats['q25']:.2f} 手")
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print(f"75%分位数: {self.stats['q75']:.2f} 手")
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print(f"90%分位数: {self.stats['q90']:.2f} 手")
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print(f"95%分位数: {self.stats['q95']:.2f} 手")
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print(f"99%分位数: {self.stats['q99']:.2f} 手")
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# 计算变异系数
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cv = self.stats['std_volume'] / self.stats['mean_volume']
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print(f"变异系数 (CV): {cv:.3f}")
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# 检查分布偏度
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skewness = stats.skew(self.current_volume_clean)
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print(f"分布偏度: {skewness:.3f}")
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self.stats['cv'] = cv
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self.stats['skewness'] = skewness
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def analyze_volume_patterns(self):
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"""分析成交量模式"""
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print("\n=== 成交量模式分析 ===")
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# 分析大单交易
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large_threshold = self.stats['q90']
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large_trades = self.current_volume_clean[self.current_volume_clean > large_threshold]
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print(f"大单交易定义: > {large_threshold:.1f} 手 (90%分位数)")
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print(f"大单交易次数: {len(large_trades):,}")
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print(f"大单交易占比: {len(large_trades)/len(self.current_volume_clean)*100:.2f}%")
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print(f"大单成交量总和: {large_trades.sum():,} 手")
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print(f"大单成交量占比: {large_trades.sum()/self.current_volume_clean.sum()*100:.2f}%")
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# 分析成交量聚集模式
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volume_groups = pd.cut(self.current_volume_clean,
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bins=[0, 1, 5, 10, 20, 50, 100, float('inf')],
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labels=['1手', '2-5手', '6-10手', '11-20手', '21-50手', '51-100手', '100+手'])
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print(f"\n成交量分组统计:")
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for group_name, group_data in self.current_volume_clean.groupby(volume_groups):
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if not pd.isna(group_name):
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count = len(group_data)
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percentage = count / len(self.current_volume_clean) * 100
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volume_sum = group_data.sum()
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volume_percentage = volume_sum / self.current_volume_clean.sum() * 100
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print(f" {group_name:>6}: {count:>6,} 次 ({percentage:>5.1f}%) | "
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f"成交量: {volume_sum:>8,.0f} 手 ({volume_percentage:>5.1f}%)")
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# 存储模式分析结果
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self.pattern_analysis = {
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'large_threshold': large_threshold,
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'large_trades_count': len(large_trades),
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'large_trades_percentage': len(large_trades)/len(self.current_volume_clean)*100,
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'large_volume_sum': large_trades.sum(),
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'large_volume_percentage': large_trades.sum()/self.current_volume_clean.sum()*100,
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'volume_groups': volume_groups,
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'group_stats': {}
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}
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for group_name, group_data in self.current_volume_clean.groupby(volume_groups):
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if not pd.isna(group_name):
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self.pattern_analysis['group_stats'][group_name] = {
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'count': len(group_data),
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'percentage': len(group_data)/len(self.current_volume_clean)*100,
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'volume_sum': group_data.sum(),
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'volume_percentage': group_data.sum()/self.current_volume_clean.sum()*100
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}
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def create_distribution_charts(self):
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"""创建分布图表"""
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print("\n=== 生成成交量分布图表 ===")
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# 确保字体设置正确
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plt.rcParams['font.sans-serif'] = plt.rcParams['font.sans-serif']
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plt.rcParams['axes.unicode_minus'] = False
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# 创建复合图表布局
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fig = plt.figure(figsize=(20, 16))
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gs = GridSpec(3, 3, figure=fig, hspace=0.3, wspace=0.3)
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# 1. 直方图 + 概率密度图
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ax1 = fig.add_subplot(gs[0, 0])
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n, bins, patches = ax1.hist(self.current_volume_clean, bins=50,
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alpha=0.7, color='skyblue', density=True,
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edgecolor='black', linewidth=0.5)
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# 拟合正态分布
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mu, sigma = self.stats['mean_volume'], self.stats['std_volume']
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x = np.linspace(self.current_volume_clean.min(), self.current_volume_clean.max(), 100)
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ax1.plot(x, stats.norm.pdf(x, mu, sigma), 'r-', linewidth=2,
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label=f'正态分布拟合\n(μ={mu:.1f}, σ={sigma:.1f})')
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ax1.set_title('当前成交量分布直方图', fontsize=14, fontweight='bold')
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ax1.set_xlabel('成交量 (手)', fontsize=12)
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ax1.set_ylabel('概率密度', fontsize=12)
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ax1.legend(fontsize=10)
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ax1.grid(True, alpha=0.3)
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# 2. 箱线图
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ax2 = fig.add_subplot(gs[0, 1])
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boxplot_data = [self.current_volume_clean[self.current_volume_clean <= q]
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for q in [self.stats['q90'], self.stats['q95'], self.stats['q99']]]
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boxplot_labels = ['90%分位数内', '95%分位数内', '99%分位数内']
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bp = ax2.boxplot(boxplot_data, labels=boxplot_labels, patch_artist=True)
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for patch in bp['boxes']:
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patch.set_facecolor('lightgreen')
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patch.set_alpha(0.7)
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ax2.set_title('当前成交量箱线图', fontsize=14, fontweight='bold')
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ax2.set_ylabel('成交量 (手)', fontsize=12)
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ax2.grid(True, alpha=0.3)
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# 3. 累积分布函数
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ax3 = fig.add_subplot(gs[0, 2])
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sorted_data = np.sort(self.current_volume_clean)
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cumulative = np.arange(1, len(sorted_data) + 1) / len(sorted_data)
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ax3.plot(sorted_data, cumulative, linewidth=2, color='purple')
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ax3.set_title('当前成交量累积分布函数', fontsize=14, fontweight='bold')
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ax3.set_xlabel('成交量 (手)', fontsize=12)
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ax3.set_ylabel('累积概率', fontsize=12)
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ax3.grid(True, alpha=0.3)
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ax3.axhline(y=0.5, color='red', linestyle='--', alpha=0.7, label='中位数')
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ax3.axhline(y=0.9, color='orange', linestyle='--', alpha=0.7, label='90%分位数')
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ax3.legend(fontsize=10)
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# 4. 成交量时间序列
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ax4 = fig.add_subplot(gs[1, :])
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sample_indices = np.linspace(0, len(self.current_volume)-1, 1000, dtype=int)
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ax4.plot(sample_indices, self.current_volume.iloc[sample_indices],
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linewidth=0.8, alpha=0.8, color='steelblue')
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ax4.fill_between(sample_indices, 0, self.current_volume.iloc[sample_indices],
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alpha=0.3, color='steelblue')
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ax4.set_title('当前成交量时间序列 (采样显示)', fontsize=14, fontweight='bold')
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ax4.set_xlabel('时间序列索引', fontsize=12)
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ax4.set_ylabel('成交量 (手)', fontsize=12)
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ax4.grid(True, alpha=0.3)
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# 添加大单标记
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large_threshold = self.stats['q90']
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large_indices = np.where(self.current_volume > large_threshold)[0]
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if len(large_indices) > 0:
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sample_large_indices = np.intersect1d(sample_indices, large_indices)
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if len(sample_large_indices) > 0:
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ax4.scatter(sample_large_indices, self.current_volume.iloc[sample_large_indices],
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color='red', s=20, alpha=0.8, zorder=5,
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label=f'大单交易 (>{large_threshold:.0f}手)')
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ax4.legend(fontsize=10)
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# 5. 成交量分组柱状图
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ax5 = fig.add_subplot(gs[2, 0])
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group_names = list(self.pattern_analysis['group_stats'].keys())
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group_counts = [self.pattern_analysis['group_stats'][name]['count']
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for name in group_names]
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colors = plt.cm.Set3(np.linspace(0, 1, len(group_names)))
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bars = ax5.bar(group_names, group_counts, color=colors, alpha=0.8, edgecolor='black', linewidth=0.5)
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# 在柱子上添加数值标签
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for bar, count in zip(bars, group_counts):
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height = bar.get_height()
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ax5.text(bar.get_x() + bar.get_width()/2., height + max(group_counts)*0.01,
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f'{count:,}', ha='center', va='bottom', fontsize=10, fontweight='bold')
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ax5.set_title('成交量分组分布 (按交易笔数)', fontsize=14, fontweight='bold')
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ax5.set_xlabel('成交量分组', fontsize=12)
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ax5.set_ylabel('交易笔数', fontsize=12)
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ax5.grid(True, alpha=0.3, axis='y')
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# 旋转x轴标签以便更好地显示
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plt.setp(ax5.get_xticklabels(), rotation=45, ha='right')
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# 6. 成交量贡献度条形图
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ax6 = fig.add_subplot(gs[2, 1])
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volume_contributions = [self.pattern_analysis['group_stats'][name]['volume_percentage']
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for name in group_names]
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bars = ax6.barh(group_names, volume_contributions, color=colors, alpha=0.7)
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ax6.set_title('成交量贡献度 (按成交量占比)', fontsize=14, fontweight='bold')
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ax6.set_xlabel('成交量占比 (%)', fontsize=12)
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ax6.grid(True, alpha=0.3, axis='x')
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# 在条形上添加数值标签
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for i, (bar, contribution) in enumerate(zip(bars, volume_contributions)):
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ax6.text(contribution + 0.5, bar.get_y() + bar.get_height()/2,
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f'{contribution:.1f}%', va='center', fontsize=10)
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# 7. 统计摘要表
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ax7 = fig.add_subplot(gs[2, 2])
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ax7.axis('off')
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# 创建统计摘要文本
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summary_text = f"""成交量统计摘要
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数据量: {self.stats['total_ticks']:,} Tick
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有效成交量: {self.stats['valid_ticks']:,} Tick
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基础统计:
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* 平均值: {self.stats['mean_volume']:.2f} 手
|
||
* 中位数: {self.stats['median_volume']:.2f} 手
|
||
* 标准差: {self.stats['std_volume']:.2f} 手
|
||
* 变异系数: {self.stats['cv']:.3f}
|
||
* 偏度: {self.stats['skewness']:.3f}
|
||
|
||
分位数统计:
|
||
* 最小值: {self.stats['min_volume']:.2f} 手
|
||
* 25%分位: {self.stats['q25']:.2f} 手
|
||
* 75%分位: {self.stats['q75']:.2f} 手
|
||
* 90%分位: {self.stats['q90']:.2f} 手
|
||
* 95%分位: {self.stats['q95']:.2f} 手
|
||
* 最大值: {self.stats['max_volume']:.2f} 手
|
||
|
||
大单交易分析:
|
||
* 大单阈值: {self.pattern_analysis['large_threshold']:.1f} 手
|
||
* 大单次数: {self.pattern_analysis['large_trades_count']:,}
|
||
* 大单占比: {self.pattern_analysis['large_trades_percentage']:.2f}%
|
||
* 大单成交量占比: {self.pattern_analysis['large_volume_percentage']:.2f}%
|
||
"""
|
||
|
||
ax7.text(0.05, 0.95, summary_text, transform=ax7.transAxes,
|
||
fontsize=10, va='top', ha='left', wrap=True,
|
||
bbox=dict(boxstyle='round,pad=0.5', facecolor='lightyellow', alpha=0.9))
|
||
|
||
# 设置总标题
|
||
fig.suptitle('AU2512期货当前成交量分布综合分析',
|
||
fontsize=18, fontweight='bold', y=0.98)
|
||
|
||
# 保存图表
|
||
plt.tight_layout()
|
||
chart_file = 'au2512_volume_distribution_analysis.png'
|
||
plt.savefig(chart_file, dpi=300, bbox_inches='tight',
|
||
facecolor='white', edgecolor='none')
|
||
plt.close()
|
||
|
||
print(f"成交量分布分析图表已保存: {chart_file}")
|
||
return chart_file
|
||
|
||
def generate_insights_report(self):
|
||
"""生成业务洞察报告"""
|
||
print("\n=== 业务洞察报告 ===")
|
||
|
||
insights = []
|
||
|
||
# 成交量分布特征分析
|
||
if self.stats['skewness'] > 1:
|
||
insights.append("1. 成交量分布呈现严重的右偏特征，表明大部分交易集中在小单，大单交易较少但影响显著")
|
||
elif self.stats['skewness'] > 0.5:
|
||
insights.append("1. 成交量分布呈现中等程度的右偏特征，存在一定比例的大单交易")
|
||
else:
|
||
insights.append("1. 成交量分布相对均匀，大小单交易分布较为均衡")
|
||
|
||
# 变异系数分析
|
||
if self.stats['cv'] > 2:
|
||
insights.append("2. 成交量波动极大，市场交易活跃度变化剧烈，需要关注市场情绪变化")
|
||
elif self.stats['cv'] > 1:
|
||
insights.append("2. 成交量波动较大，市场存在明显的活跃期和沉寂期")
|
||
else:
|
||
insights.append("2. 成交量波动相对稳定，市场交易活跃度较为一致")
|
||
|
||
# 大单交易分析
|
||
if self.pattern_analysis['large_volume_percentage'] > 50:
|
||
insights.append("3. 大单交易占据主导地位，可能表明机构投资者参与度较高")
|
||
elif self.pattern_analysis['large_volume_percentage'] > 30:
|
||
insights.append("3. 大单交易占比较为重要，对市场流动性有显著影响")
|
||
else:
|
||
insights.append("3. 小单交易为主，市场参与者可能以散户为主")
|
||
|
||
# 交易模式分析
|
||
dominant_group = max(self.pattern_analysis['group_stats'].items(),
|
||
key=lambda x: x[1]['volume_percentage'])
|
||
insights.append(f"4. 主要成交量贡献来自{dominant_group[0]}的交易，占比{dominant_group[1]['volume_percentage']:.1f}%")
|
||
|
||
# 市场活跃度分析
|
||
if self.stats['mean_volume'] > 20:
|
||
insights.append("5. 市场整体活跃度较高，平均单笔成交量较大")
|
||
elif self.stats['mean_volume'] > 10:
|
||
insights.append("5. 市场活跃度适中，交易较为频繁")
|
||
else:
|
||
insights.append("5. 市场活跃度相对较低，交易较为谨慎")
|
||
|
||
# 风险提示
|
||
if self.stats['max_volume'] / self.stats['mean_volume'] > 50:
|
||
insights.append("6. 风险提示：存在超大单交易，可能导致价格剧烈波动")
|
||
|
||
# 打印所有洞察
|
||
for insight in insights:
|
||
print(f" {insight}")
|
||
|
||
self.insights = insights
|
||
return insights
|
||
|
||
def print_detailed_report(self):
|
||
"""打印详细分析报告"""
|
||
print("\n" + "="*80)
|
||
print("AU2512期货当前成交量分布分析报告")
|
||
print("="*80)
|
||
|
||
# 数据概况
|
||
print(f"\n【数据概况】")
|
||
print(f"数据文件: {self.data_file}")
|
||
print(f"分析时间: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")
|
||
print(f"总记录数: {self.stats['total_ticks']:,}")
|
||
print(f"有效成交量记录: {self.stats['valid_ticks']:,}")
|
||
print(f"数据完整性: {self.stats['valid_ticks']/self.stats['total_ticks']*100:.2f}%")
|
||
|
||
# 基础统计
|
||
print(f"\n【基础统计】")
|
||
print(f"平均成交量: {self.stats['mean_volume']:.2f} 手")
|
||
print(f"中位数成交量: {self.stats['median_volume']:.2f} 手")
|
||
print(f"成交量标准差: {self.stats['std_volume']:.2f} 手")
|
||
print(f"变异系数: {self.stats['cv']:.3f}")
|
||
print(f"分布偏度: {self.stats['skewness']:.3f}")
|
||
|
||
# 分位数分析
|
||
print(f"\n【分位数分析】")
|
||
percentiles = [0, 25, 50, 75, 90, 95, 99, 100]
|
||
percentile_names = ['最小值', '25%分位', '中位数', '75%分位', '90%分位', '95%分位', '99%分位', '最大值']
|
||
for name, pct in zip(percentile_names, percentiles):
|
||
value = self.current_volume_clean.quantile(pct/100) if pct > 0 else self.stats['min_volume']
|
||
if pct == 100:
|
||
value = self.stats['max_volume']
|
||
print(f"{name:>8}: {value:>8.2f} 手")
|
||
|
||
# 大单交易分析
|
||
print(f"\n【大单交易分析】")
|
||
print(f"大单阈值 (90%分位数): {self.pattern_analysis['large_threshold']:.1f} 手")
|
||
print(f"大单交易次数: {self.pattern_analysis['large_trades_count']:,}")
|
||
print(f"大单交易占比: {self.pattern_analysis['large_trades_percentage']:.2f}%")
|
||
print(f"大单成交量占比: {self.pattern_analysis['large_volume_percentage']:.2f}%")
|
||
|
||
# 成交量分组分析
|
||
print(f"\n【成交量分组分析】")
|
||
print(f"{'分组':>8} {'次数':>8} {'占比':>8} {'成交量':>10} {'占比':>8}")
|
||
print("-" * 50)
|
||
for group_name in self.pattern_analysis['group_stats']:
|
||
stats = self.pattern_analysis['group_stats'][group_name]
|
||
print(f"{group_name:>8} {stats['count']:>8,} {stats['percentage']:>7.1f}% "
|
||
f"{stats['volume_sum']:>10,.0f} {stats['volume_percentage']:>7.1f}%")
|
||
|
||
# 业务洞察
|
||
print(f"\n【业务洞察】")
|
||
for insight in self.insights:
|
||
print(f" {insight}")
|
||
|
||
# 交易建议
|
||
print(f"\n【交易建议】")
|
||
if self.stats['cv'] > 2:
|
||
print(" * 建议关注市场情绪变化，适时调整交易策略")
|
||
if self.pattern_analysis['large_volume_percentage'] > 40:
|
||
print(" * 关注大单流向，可能预示价格趋势变化")
|
||
if self.stats['mean_volume'] < 10:
|
||
print(" * 市场活跃度较低，建议谨慎交易")
|
||
else:
|
||
print(" * 市场活跃度适中，适合正常交易策略")
|
||
|
||
print(f"\n【生成的文件】")
|
||
print(f" au2512_volume_distribution_analysis.png - 成交量分布综合分析图表")
|
||
|
||
print(f"\n" + "="*80)
|
||
print("分析完成！")
|
||
print("="*80)
|
||
|
||
def run_analysis(self):
|
||
"""运行完整分析"""
|
||
print("开始AU2512期货当前成交量分布分析...")
|
||
|
||
# 加载数据
|
||
if not self.load_data():
|
||
return False
|
||
|
||
# 准备成交量数据
|
||
if not self.prepare_volume_data():
|
||
return False
|
||
|
||
# 计算基础统计
|
||
self.calculate_basic_statistics()
|
||
|
||
# 分析成交量模式
|
||
self.analyze_volume_patterns()
|
||
|
||
# 创建图表
|
||
self.create_distribution_charts()
|
||
|
||
# 生成业务洞察
|
||
self.generate_insights_report()
|
||
|
||
# 打印详细报告
|
||
self.print_detailed_report()
|
||
|
||
return True
|
||
|
||
def main():
|
||
"""主函数"""
|
||
parser = argparse.ArgumentParser(description='AU2512期货当前成交量分布分析工具')
|
||
parser.add_argument('data_file', nargs='?',
|
||
default='data/au2512_20251013.parquet',
|
||
help='数据文件路径 (默认: data/au2512_20251013.parquet)')
|
||
parser.add_argument('--output-dir', '-o', default='.',
|
||
help='输出目录 (默认: 当前目录)')
|
||
|
||
args = parser.parse_args()
|
||
|
||
# 检查数据文件是否存在
|
||
if not os.path.exists(args.data_file):
|
||
print(f"错误: 数据文件不存在 - {args.data_file}")
|
||
print("\n使用方法:")
|
||
print(" python volume_distribution_analysis.py [数据文件路径]")
|
||
print(f" python volume_distribution_analysis.py # 使用默认文件: {args.data_file}")
|
||
sys.exit(1)
|
||
|
||
# 创建分析器并运行
|
||
analyzer = VolumeDistributionAnalyzer(args.data_file)
|
||
success = analyzer.run_analysis()
|
||
|
||
if success:
|
||
print(f"\n分析完成！图表已保存到: {args.output_dir}")
|
||
else:
|
||
print(f"\n分析失败！")
|
||
sys.exit(1)
|
||
|
||
if __name__ == "__main__":
|
||
main() |